scholarly journals Application of the maximum entropy principle to determine ensembles of intrinsically disordered proteins from residual dipolar couplings

2014 ◽  
Vol 16 (47) ◽  
pp. 26030-26039 ◽  
Author(s):  
M. Sanchez-Martinez ◽  
R. Crehuet
Keyword(s):  
Maximum Entropy ◽  
Residual Dipolar Couplings ◽  
Intrinsically Disordered Proteins ◽  
Protein Structures ◽  
Maximum Entropy Principle ◽  
Dipolar Couplings ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Entropy Principle

We present a method based on the maximum entropy principle that can re-weight an ensemble of protein structures based on data from residual dipolar couplings (RDCs).

Residual dipolar couplings measured in unfolded proteins are sensitive to amino-acid-specific geometries as well as local conformational sampling

2012 ◽  
Vol 40 (5) ◽  
pp. 989-994 ◽  
Author(s):  
Jie-rong Huang ◽  
Martin Gentner ◽  
Navratna Vajpai ◽  
Stephan Grzesiek ◽  
Martin Blackledge
Keyword(s):  
Amino Acid ◽  
Residual Dipolar Couplings ◽  
Intrinsically Disordered Proteins ◽  
Dipolar Couplings ◽  
Disordered Proteins ◽  
Conformational Sampling ◽  
Intrinsically Disordered ◽  
Potential Energy Landscape ◽  
Order Of Magnitude ◽  
Folded Structures

Many functional proteins do not have well defined folded structures. In recent years, both experimental and computational approaches have been developed to study the conformational behaviour of this type of protein. It has been shown previously that experimental RDCs (residual dipolar couplings) can be used to study the backbone sampling of disordered proteins in some detail. In these studies, the backbone structure was modelled using a common geometry for all amino acids. In the present paper, we demonstrate that experimental RDCs are also sensitive to the specific geometry of each amino acid as defined by energy-minimized internal co-ordinates. We have modified the FM (flexible-Meccano) algorithm that constructs conformational ensembles on the basis of a statistical coil model, to account for these differences. The modified algorithm inherits the advantages of the FM algorithm to efficiently sample the potential energy landscape for coil conformations. The specific geometries incorporated in the new algorithm result in a better reproduction of experimental RDCs and are generally applicable for further studies to characterize the conformational properties of intrinsically disordered proteins. In addition, the internal-co-ordinate-based algorithm is an order of magnitude more efficient, and facilitates side-chain construction, surface osmolyte simulation, spin-label distribution sampling and proline cis/trans isomer simulation.

scholarly journals Quantitative Determination of the Conformational Properties of Partially Folded and Intrinsically Disordered Proteins Using NMR Dipolar Couplings

Structure ◽  
2009 ◽  
Vol 17 (9) ◽  
pp. 1169-1185 ◽  
Author(s):  
Malene Ringkjøbing Jensen ◽  
Phineus R.L. Markwick ◽  
Sebastian Meier ◽  
Christian Griesinger ◽  
Markus Zweckstetter ◽  
...  
Keyword(s):  
Quantitative Determination ◽  
Intrinsically Disordered Proteins ◽  
Dipolar Couplings ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Conformational Properties

scholarly journals Co-Evolution of Intrinsically Disordered Proteins with Folded Partners Witnessed by Evolutionary Couplings

2018 ◽  
Vol 19 (11) ◽  
pp. 3315 ◽  
Author(s):  
Rita Pancsa ◽  
Fruzsina Zsolyomi ◽  
Peter Tompa
Keyword(s):  
Large Scale ◽  
Intrinsically Disordered Proteins ◽  
Protein Structures ◽  
Disordered Proteins ◽  
Cellular Interaction ◽  
Structural Constraints ◽  
Protein Residues ◽  
Intrinsically Disordered ◽  
Evolutionary Changes ◽  
Folded Proteins

Although improved strategies for the detection and analysis of evolutionary couplings (ECs) between protein residues already enable the prediction of protein structures and interactions, they are mostly restricted to conserved and well-folded proteins. Whereas intrinsically disordered proteins (IDPs) are central to cellular interaction networks, due to the lack of strict structural constraints, they undergo faster evolutionary changes than folded domains. This makes the reliable identification and alignment of IDP homologs difficult, which led to IDPs being omitted in most large-scale residue co-variation analyses. By preforming a dedicated analysis of phylogenetically widespread bacterial IDP–partner interactions, here we demonstrate that partner binding imposes constraints on IDP sequences that manifest in detectable interprotein ECs. These ECs were not detected for interactions mediated by short motifs, rather for those with larger IDP–partner interfaces. Most identified coupled residue pairs reside close (<10 Å) to each other on the interface, with a third of them forming multiple direct atomic contacts. EC-carrying interfaces of IDPs are enriched in negatively charged residues, and the EC residues of both IDPs and partners preferentially reside in helices. Our analysis brings hope that IDP–partner interactions difficult to study could soon be successfully dissected through residue co-variation analysis.

scholarly journals CheSPI: Chemical shift Secondary structure Population Inference

2021 ◽  
Author(s):  
Jakob Toudahl Nielsen ◽  
Frans A.A. Mulder
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Chemical Shifts ◽  
Protein Structures ◽  
Random Coil ◽  
Disordered Proteins ◽  
Residual Structure ◽  
Intrinsically Disordered ◽  
Population Inference ◽  
Local Protein Structure ◽  
Structural Classes

AbstractNMR chemical shifts (CSs) are delicate reporters of local protein structure, and recent advances in random coil CS (RCCS) prediction and interpretation now offer the compelling prospect of inferring small populations of structure from small deviations from RCCSs. Here, we present CheSPI, a simple and efficient method that provides unbiased and sensitive aggregate measures of local structure and disorder. It is demonstrated that CheSPI can predict even very small amounts of residual structure and robustly delineate subtle differences into four structural classes for intrinsically disordered proteins. For structured regions and proteins, CheSPI can assign up to eight structural classes, which coincide with the well-known DSSP classification. The program is freely available, and can either be invoked from URL www.protein-nmr.org as a web implementation, or run locally from command line as a python program. CheSPI generates comprehensive numeric and graphical output for intuitive annotation and visualization of protein structures. A number of examples are provided.

scholarly journals Distance-based metrics for comparing conformational ensembles of intrinsically disordered proteins

2020 ◽  
Author(s):  
Tamas Lazar ◽  
Mainak Guharoy ◽  
Wim Vranken ◽  
Sarah Rauscher ◽  
Shoshana J. Wodak ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Intrinsically Disordered Proteins ◽  
Statistical Significance ◽  
Protein Structures ◽  
Disordered Proteins ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Conformational Ensembles ◽  
Intrinsically Disordered

AbstractIntrinsically disordered proteins (IDPs) are proteins whose native functional states represent ensembles of highly diverse conformations. Such ensembles are a challenge for quantitative structure comparisons as their conformational diversity precludes optimal superimposition of the atomic coordinates, necessary for deriving common similarity measures such as the root-mean-square deviation (RMSD) of these coordinates. Here we introduce superimposition-free metrics, which are based on computing matrices of Cα-Cα distance distributions within ensembles and comparing these matrices between ensembles. Differences between two matrices yield information on the similarity between specific regions of the polypeptide, whereas the global structural similarity is captured by the ens_dRMS, defined as the root-mean-square difference between the medians of the Cα-Cαdistance distributions of two ensembles. Together, our metrics enable rigorous investigations of structure-function relationships in conformational ensembles of IDPs derived using experimental restraints or by molecular simulations, and for proteins containing both structured and disordered regions.Statement of SignificanceImportant biological insight is obtained from comparing the high-resolution structures of proteins. Such comparisons commonly involve superimposing two protein structures and computing the residual root-mean-square deviation of the atomic positions. This approach cannot be applied to intrinsically disordered proteins (IDPs) because IDPs do not adopt well-defined 3D structures, rather, their native functional state is defined by ensembles of heterogeneous conformations that cannot be meaningfully superimposed. We report new measures that quantify the local and global similarity between different conformational ensembles by evaluating differences between the distributions of residue-residue distances and their statistical significance. Applying these measures to IDP ensembles and to a protein containing both structured and intrinsically disordered domains provides deeper insights into how structural features relate to function.

scholarly journals Comparative roles of charge,π, and hydrophobic interactions in sequence-dependent phase separation of intrinsically disordered proteins

2020 ◽  
Vol 117 (46) ◽  
pp. 28795-28805
Author(s):  
Suman Das ◽  
Yi-Hsuan Lin ◽  
Robert M. Vernon ◽  
Julie D. Forman-Kay ◽  
Hue Sun Chan
Keyword(s):  
Phase Separation ◽  
Electrostatic Interactions ◽  
Intrinsically Disordered Proteins ◽  
Protein Structures ◽  
Coarse Grained ◽  
Disordered Proteins ◽  
Chain Model ◽  
Statistical Potentials ◽  
Intrinsically Disordered ◽  
Sequence Dependent

Endeavoring toward a transferable, predictive coarse-grained explicit-chain model for biomolecular condensates underlain by liquid–liquid phase separation (LLPS) of proteins, we conducted multiple-chain simulations of the N-terminal intrinsically disordered region (IDR) of DEAD-box helicase Ddx4, as a test case, to assess roles of electrostatic, hydrophobic, cation–π, and aromatic interactions in amino acid sequence-dependent LLPS. We evaluated three different residue–residue interaction schemes with a shared electrostatic potential. Neither a common hydrophobicity scheme nor one augmented with arginine/lysine-aromatic cation–π interactions consistently accounted for available experimental LLPS data on the wild-type, a charge-scrambled, a phenylalanine-to-alanine (FtoA), and an arginine-to-lysine (RtoK) mutant of Ddx4 IDR. In contrast, interactions based on contact statistics among folded globular protein structures reproduce the overall experimental trend, including that the RtoK mutant has a much diminished LLPS propensity. Consistency between simulation and experiment was also found for RtoK mutants of P-granule protein LAF-1, underscoring that, to a degree, important LLPS-driving π-related interactions are embodied in classical statistical potentials. Further elucidation is necessary, however, especially of phenylalanine’s role in condensate assembly because experiments on FtoA and tyrosine-to-phenylalanine mutants suggest that LLPS-driving phenylalanine interactions are significantly weaker than posited by common statistical potentials. Protein–protein electrostatic interactions are modulated by relative permittivity, which in general depends on aqueous protein concentration. Analytical theory suggests that this dependence entails enhanced interprotein interactions in the condensed phase but more favorable protein–solvent interactions in the dilute phase. The opposing trends lead to only a modest overall impact on LLPS.

scholarly journals RFPR-IDP: reduce the false positive rates for intrinsically disordered protein and region prediction by incorporating both fully ordered proteins and disordered proteins

2020 ◽  
Author(s):  
Yumeng Liu ◽  
Xiaolong Wang ◽  
Bin Liu
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Short Term Memory ◽  
Protein Structures ◽  
Intrinsically Disordered Protein ◽  
Disordered Proteins ◽  
Test Dataset ◽  
Intrinsically Disordered ◽  
Disordered Protein ◽  
Real World Applications ◽  
Long Short Term Memory

Abstract As an important type of proteins, intrinsically disordered proteins/regions (IDPs/IDRs) are related to many crucial biological functions. Accurate prediction of IDPs/IDRs is beneficial to the prediction of protein structures and functions. Most of the existing methods ignore the fully ordered proteins without IDRs during training and test processes. As a result, the corresponding predictors prefer to predict the fully ordered proteins as disordered proteins. Unfortunately, these methods were only evaluated on datasets consisting of disordered proteins without or with only a few fully ordered proteins, and therefore, this problem escapes the attention of the researchers. However, most of the newly sequenced proteins are fully ordered proteins in nature. These predictors fail to accurately predict the ordered and disordered proteins in real-world applications. In this regard, we propose a new method called RFPR-IDP trained with both fully ordered proteins and disordered proteins, which is constructed based on the combination of convolution neural network (CNN) and bidirectional long short-term memory (BiLSTM). The experimental results show that although the existing predictors perform well for predicting the disordered proteins, they tend to predict the fully ordered proteins as disordered proteins. In contrast, the RFPR-IDP predictor can correctly predict the fully ordered proteins and outperform the other 10 state-of-the-art methods when evaluated on a test dataset with both fully ordered proteins and disordered proteins. The web server and datasets of RFPR-IDP are freely available at http://bliulab.net/RFPR-IDP/server.

Sign in / Sign up

Export Citation Format

Share Document